from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available


_SCREAMING_SNAKE_CASE     				=		{
    'configuration_graphormer': ['GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP', 'GraphormerConfig'],
}

try:
    if not is_torch_available():
        raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
    pass
else:
    _SCREAMING_SNAKE_CASE     				=		[
        'GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST',
        'GraphormerForGraphClassification',
        'GraphormerModel',
        'GraphormerPreTrainedModel',
    ]


if TYPE_CHECKING:
    from .configuration_graphormer import GRAPHORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, GraphormerConfig

    try:
        if not is_torch_available():
            raise OptionalDependencyNotAvailable()
    except OptionalDependencyNotAvailable:
        pass
    else:
        from .modeling_graphormer import (
            GRAPHORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
            GraphormerForGraphClassification,
            GraphormerModel,
            GraphormerPreTrainedModel,
        )


else:
    import sys

    _SCREAMING_SNAKE_CASE     				=		_LazyModule(__name__, globals()['__file__'], _import_structure, module_spec=__spec__)





import numpy as np
import torch
import tqdm

from ...models.unet_ad import UNetaDModel
from ...pipelines import DiffusionPipeline
from ...utils import randn_tensor
from ...utils.dummy_pt_objects import DDPMScheduler




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 def __init__(		self				:   int							, lowercase_				:   UNetaDModel							, lowercase_				:   UNetaDModel							, lowercase_				:   DDPMScheduler							, lowercase_				:   Any							, )						->	Any:


   """simple docstring"""







   super().__init__()
   _UpperCamelCase           =    value_function
   _UpperCamelCase           =    unet
   _UpperCamelCase           =    scheduler
   _UpperCamelCase           =    env
   _UpperCamelCase           =    env.get_dataset()
   _UpperCamelCase           =    {}
   for key in self.data.keys():
     try:
       _UpperCamelCase           =    self.data[key].mean()
     except:  # noqa: E722
       pass
   _UpperCamelCase           =    {}
   for key in self.data.keys():
     try:
       _UpperCamelCase           =    self.data[key].std()
     except:  # noqa: E722
       pass
   _UpperCamelCase           =    env.observation_space.shape[0]
   _UpperCamelCase           =    env.action_space.shape[0]







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Optional[int]							, lowercase_				:   Optional[Any])						->	Tuple:


   """simple docstring"""







   return (x_in - self.means[key]) / self.stds[key]







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   Tuple							, lowercase_				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   return x_in * self.stds[key] + self.means[key]







 def 		__UpperCAmelCase				(		self				:   List[Any]							, lowercase_				:   Optional[Any])						->	List[Any]:


   """simple docstring"""







   if type(lowercase_) is dict:
     return {k: self.to_torch(lowercase_) for k, v in x_in.items()}
   elif torch.is_tensor(lowercase_):
     return x_in.to(self.unet.device)
   return torch.tensor(lowercase_							, device=self.unet.device)







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   Any							, lowercase_				:   Union[str, Any]							, lowercase_				:   List[str])						->	Tuple:


   """simple docstring"""







   for key, val in cond.items():
     _UpperCamelCase           =    val.clone()
   return x_in







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, lowercase_				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   List[Any]							, lowercase_				:   int)						->	Dict:


   """simple docstring"""







   _UpperCamelCase           =    x.shape[0]
   _UpperCamelCase           =    None
   for i in tqdm.tqdm(self.scheduler.timesteps):
     # create batch of timesteps to pass into model
     _UpperCamelCase           =    torch.full((batch_size,)							, lowercase_							, device=self.unet.device							, dtype=torch.long)
     for _ in range(lowercase_):
       with torch.enable_grad():
         x.requires_grad_()

         # permute to match dimension for pre-trained models
         _UpperCamelCase           =    self.value_function(x.permute(0							, 2							, 1)							, lowercase_).sample
         _UpperCamelCase           =    torch.autograd.grad([y.sum()]							, [x])[0]

         _UpperCamelCase           =    self.scheduler._get_variance(lowercase_)
         _UpperCamelCase           =    torch.exp(0.5 * posterior_variance)
         _UpperCamelCase           =    model_std * grad

       _UpperCamelCase           =    0
       _UpperCamelCase           =    x.detach()
       _UpperCamelCase           =    x + scale * grad
       _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)

     _UpperCamelCase           =    self.unet(x.permute(0							, 2							, 1)							, lowercase_).sample.permute(0							, 2							, 1)

     # TODO: verify deprecation of this kwarg
     _UpperCamelCase           =    self.scheduler.step(lowercase_							, lowercase_							, lowercase_							, predict_epsilon=lowercase_)["prev_sample"]

     # apply conditions to the trajectory (set the initial state)
     _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)
     _UpperCamelCase           =    self.to_torch(lowercase_)
   return x, y







 def __call__(		self				:   Optional[int]							, lowercase_				:   str							, lowercase_				:   int=64							, lowercase_				:   Any=32							, lowercase_				:   List[Any]=2							, lowercase_				:   str=0.1)						->	Optional[int]:


   """simple docstring"""







   _UpperCamelCase           =    self.normalize(lowercase_							, "observations")
   _UpperCamelCase           =    obs[None].repeat(lowercase_							, axis=0)

   _UpperCamelCase           =    {0: self.to_torch(lowercase_)}
   _UpperCamelCase           =    (batch_size, planning_horizon, self.state_dim + self.action_dim)

   # generate initial noise and apply our conditions (to make the trajectories start at current state)
   _UpperCamelCase           =    randn_tensor(lowercase_							, device=self.unet.device)
   _UpperCamelCase           =    self.reset_xa(lowercase_							, lowercase_							, self.action_dim)
   _UpperCamelCase           =    self.to_torch(lowercase_)

   # run the diffusion process
   _UpperCamelCase      ,						_UpperCamelCase           =    self.run_diffusion(lowercase_							, lowercase_							, lowercase_							, lowercase_)

   # sort output trajectories by value
   _UpperCamelCase           =    y.argsort(0							, descending=lowercase_).squeeze()
   _UpperCamelCase           =    x[sorted_idx]
   _UpperCamelCase           =    sorted_values[:, :, : self.action_dim]
   _UpperCamelCase           =    actions.detach().cpu().numpy()
   _UpperCamelCase           =    self.de_normalize(lowercase_							, key="actions")

   # select the action with the highest value
   if y is not None:
     _UpperCamelCase           =    0
   else:
     # if we didn't run value guiding, select a random action
     _UpperCamelCase           =    np.random.randint(0							, lowercase_)

   _UpperCamelCase           =    denorm_actions[selected_index, 0]
   return denorm_actions




